Quant Regime Strategy (Independent Study)

Full quant research pipeline: heavy tails, volatility clustering, HMM, GARCH, momentum.

About this project

Personal Project, Dec 2025 – Feb 2026. Built a full quantitative research pipeline on 6 years of daily equity returns. Heavy tails—empirical tail frequency is 5x higher than Gaussian models predict. Volatility clustering—ACF of |returns| significant out to 50+ lags. HMM regime detection—two latent states with annualised vols of 17% vs 43%. GARCH(1,1) volatility forecasting (RMSE: 0.00293). Combining regime signals and GARCH filters into a momentum strategy lifted the Sharpe ratio from 0.52 to 0.90 and cut maximum drawdown from -39.7% to -13.8%. Key takeaway: you don't need complex ML to beat a naive benchmark—just take the empirical properties of returns seriously.